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Record W2924600801 · doi:10.1093/heapro/daz015

Community health agents, nurses and physicians conducting research in Brazil’s family health program

2019· article· en· W2924600801 on OpenAlexaff
Rahbel Rahman, Rogério M. Pinto, Margareth Santos Zanchetta, Joanna Lu, Renee Bailey

Bibliographic record

VenueHealth Promotion International · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCommunity healthNursingData collectionPublic healthScale (ratio)PsychologyPerceptionMedical educationFamily medicineMedicineSociology

Abstract

fetched live from OpenAlex

While the integration of community-based providers within interprofessional health teams has been recommended by policymakers worldwide, there is limited research on how medical and community-based providers inform and participate in health research. Our study uses cross-sectional data from 169 Community Health Workers (CHWs), 62 nurses, and 31 physicians within Brazil's Family Health Strategy Program. Using an integrated framework of social cognitive theories and Theory for Planned Behavior, a reliable and valid instrument was developed to examine differences in past research involvement, and opinions about health and public health research (research efficacy and perceptions of research process). Descriptive frequencies and ANOVA F-tests were performed. Results indicated that CHWs has greater mistrust in the research process, and were not involved in substantive aspects of research (specification of aims, data collection, analysis, dissemination). Nurses compared to CHWs recruited participants to research studies, and had greater willingness to learn, participate and implement research initiatives. Physicians compared to CHWs and nurses developed survey instruments and disseminated research. For community-based and medical providers to be involved in all aspects of research, researchers ought to set up structured infrastructures of community collaborative boards. Furthermore, researchers can test our scale with other providers working within health teams globally.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.850
GPT teacher head0.763
Teacher spread0.087 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2019
Admission routes1
Has abstractyes

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